Chapter outline
  • Filters can be used to reduce noise and/or enhance features, making detection & measurement much easier

  • Linear filters replace each pixel by a weighted sum of surrounding pixels

  • Nonlinear filters replace each pixel with the result of some other computation using surrounding pixels

  • Gaussian filters have various advantages that make them a good choice for many applications with fluorescence images


Filters are phenomenally useful. Almost all interesting image analysis involves filtering of some sort at some stage. In fact, the analysis of a difficult image sometimes becomes trivial once a suitable filter has been applied to it. It is therefore no surprise that much of the image processing literature is devoted to the topic of designing and testing filters.

The basic idea of filtering here is that each pixel in an image is assigned a new value depending upon the values of other pixels within some defined region (the pixel’s neighborhood). Different filters work by applying different calculations to the neighborhood to get their output. Although the plethora of available filters can be intimidating at first, knowing only a few of the most useful is already a huge advantage.

This chapter begins by introducing several extremely common linear and nonlinear filters for image processing. It ends by considering in detail some techniques based on one particularly important linear filter.

Linear filtering

Linear filters replace each pixel with a linear combination ('sum of products') of other pixels. Therefore the only mathematical background they require is the ability to add and multiply.

Mean filters

To begin, consider the somewhat noisy image of a yeast cell in Figure 1(A).

yeast mean.png
yeast mean subtract.png
yeast plot.png
A: Original image
yeast mean plot.png
B: Mean filtered
yeast mean subtract plot.png
C: Subtraction (A) - (B)

Figure 1: Filters can be used to reduce noise. A spinning disc confocal image of a yeast cell. Applying a small mean filter makes the image smoother, as is particularly evident in the fluorescence plot made through the image center. Computing the difference between images shows what the filter removed, which was mostly random noise.

The noise can be seen in the random jumps in the fluorescence intensity profile shown. One way to improve this is to take each pixel, and simply replace its value with the mean (average) of itself and the 8 pixels immediately beside it (including diagonals). This 'averages out' much of this noisy variation, giving a result that is considerably smoother (B). Subtracting the smoothed image from the original shows that what has been removed consists of small positive and negative values, mostly (but not entirely) lacking in interesting structure (C).

This smoothing is what a 3 × 3 mean filter[1] does. Each new pixel now depends upon the average of the values in a 3 × 3 pixel region: the noise is reduced, at a cost of only a little spatial information. The easiest way to apply this in ImageJ is through the Process ▸ Smooth command[2]. But this simple filter could be easily modified in at least two ways:

  1. Its size could be increased. For example, instead of using just the pixels immediately adjacent to the one we are interested in, a 5 × 5 mean filter replaces each pixel by the average of a square containing 25 pixels, still centered on the main pixel of interest.

  2. The average of the pixels in some other shape of region could be computed, not just an n × n square.

Process ▸ Filters ▸ Mean…​ is ImageJ’s general command for mean filtering. It uses approximately circular neighborhoods, and the neighborhood size is adjusted by choosing a Radius value. The Show Circular Masks command displays the neighborhoods used for different values of Radius. If you happen to choose Radius = 1, you get a 3 × 3 filter – and the same results as using Smooth.

yeast mean radius orig.png
yeast mean radius 1.png
yeast mean radius 5.png
yeast mean radius 10.png
yeast mean radius orig plot.png
A: Original image
yeast mean radius 1 plot.png
B: Filtered, radius=1
yeast mean radius 5 plot.png
C: Filtered, radius=5
yeast mean radius 10 plot.png
D: Filtered, radius=10

Figure 2: Smoothing an image using mean filters with different radii.

Figure 2 shows that as the radius increases, the image becomes increasingly smooth – losing detail along with noise. This causes the result to look blurry. If noise reduction is the primary goal, it is therefore best to avoid unnecessary blurring by using the smallest filter that gives acceptable results. More details on why mean filters reduce noise, and by how much, will be given in the chapter on Noise.


Setting Radius = 6 gives a circular filter that replaces each pixel with the mean of 121 pixels. Using a square 11 × 11 filter would also replace each pixel with the mean of 121 pixels. Can you think of any advantages in using the circular filter rather than the square filter?


Circles are more 'compact'. Every point on the perimeter of a circle is the same distance from the center. Therefore using a circular filter involves calculating the mean of all pixels a distance of Radius pixels away from the center. For a square filter, pixels that are further away in diagonal directions than horizontal or vertical directions are allowed to influence the results. If a pixel is further away, it is more likely to have a very different value because it is part of some other structure.

General linear filters

There are various ways to compute a mean of N different pixels. One is to add up all the values, then divide the result by N. Another is to multiply each value by 1/N, then add up the results. The second approach has the advantage that it is easy to modify to achieve a different outcome by changing the weights used to scale each pixel depending upon where it is. This is how a linear filter works in general, and mean filters are simply one specific example.

filter mean 9.png
A: 3 × 3 square
filter mean 25.png
B: 5 × 5 square
filter mean 13.png
C: Circular, radius = 1.5
filter mean 21.png
D: Circular, radius = 2

Figure 3: The kernels used with several mean filters. Note that (C) and (D) are the 'circular' filters used by ImageJ’s Mean…​ command for different radii.

A linear filter is defined by a filter kernel (or filter mask). This resembles another (usually small and rectangular) image in which each pixel is known as a filter coefficient and these correspond to the the weights used for scaling. In the case of a mean filter, the coefficients are all the same (or zero, if the pixel is not part of the neighborhood), as shown in Figure 3. But different kernels can give radically different results, and be designed to have very specific properties.

An algorithm to apply the filtering is shown in Figure 4.

conv 1.png
A: The filter is positioned over the top corner of the image. The products of the filter coefficients and the corresponding image pixel values are added together, and the result inserted in a new output image (although here the output is displayed in the original image to save space).
conv 2.png
B: The filter is shifted to the next pixel in line, and the process repeated.
conv 3.png
C: The filtering continues into the third pixel.
conv end.png
D: The filtering operation is applied to all pixels in the image to produce the final output.

Figure 4: Applying a linear filter to an image containing two non-zero pixels using the sum-of-products algorithm. The result is an image that looks like it contains two (scaled) versions of the filter itself, which in this case overlap with one another.


When filtering, the output for each pixel is usually put into a new image – so that the original image is unchanged (although ImageJ might switch the new image in place of the old as soon as it is finished, so that it looks like the image was changed).

Is the creation of a new image really necessary for the algorithm in Figure 4 to work, or does it just prevent the old image being lost – allowing you to retrieve it by pressing Undo?


A new image is needed for the algorithm to work. Otherwise, if we put the results directly into the image we are still filtering then we would only be guaranteed to get the correct result for the first pixel. For later pixels, computations would involve a combination of original and already-filtered pixel values, which would most likely give a different final result.

Defining your own filters

The application of such filtering is often referred to as convolution, and if you like you can go wild inventing your own filters using the Process ▸ Filters ▸ Convolve…​ command. This allows you to choose which specific coefficients the filter should have, arranged in rows and columns. If you choose the Normalize Kernel option then the coefficients are scaled so that they add to 1 (if possible), by dividing by the sum of all the coefficients.


When defining an n × n filter kernel with Convolve…​, ImageJ insists that n is an odd number. Why?


If n is an odd number, the filter has a clear central pixel. This makes things easier whenever using the approach outlined in Figure 4.


Predict what happens when you convolve an image using a filter that consists of a single coefficient with a value -1 in the following cases:

  1. Normalize Kernel is checked

  2. You have a 32-bit image (Normalize Kernel is unchecked)

  3. You have an 8-bit image (Normalize Kernel is unchecked)


The results of convolving with a single -1 coefficient in different circumstances:

  1. `Normalize Kernel` is checked: Nothing at all happens. The normalization makes the filter just a single 1…​ and convolving with a single 1 leaves the image unchanged.

  2. You have a 32-bit image (Normalize Kernel unchecked): The pixel values become negative, and the image looks inverted.

  3. You have an 8-bit image (Normalize Kernel unchecked): The pixel values would become negative, but then cannot be stored in an 8-bit unsigned integer form. Therefore, all pixels simply become clipped to zero.

Gradient filters

Often, we want to detect structures in images that are distinguishable from the background because of their edges. So if we could detect the edges we would be making good progress. Because an edge is usually characterized by a relatively sharp transition in pixel values – i.e. by a steep increase or decrease in the profile across the image – gradient filters can be used to help.

A very simple gradient filter has the coefficients -1, 0, 1. Applied to an image, this replaces every pixel with the difference between the pixel to the right and the pixel to the left. The output is positive whenever the fluorescence is increasing horizontally, negative when the fluorescence is decreasing, and zero if the fluorescence is constant – no matter what the original constant value was, so that flat areas are zero in the gradient image irrespective of their original brightness. We can also rotate the filter by 90 and get a vertical gradient image (Figure 5).

gradient horizontal.png
A: Horizontal gradient
gradient vertical.png
B: Vertical gradient
gradient mag.png
C: Gradient magnitude

Figure 5: Using gradient filters and the gradient magnitude for edge enhancement.

Having two gradient images with positive and negative values can be somewhat hard to work with. If we square all the pixels in each, the values become positive. Then we can add both the horizontal and vertical images together to combine their information. If we compute the square root of the result, we get what is known as the gradient magnitude[3], which has high values around edges, and low values everywhere else. This is (almost) what is done by the command Process ▸ Find Edges.


Try calculating the gradient magnitude using Duplicate…​, Convolve…​, Image Calculator…​ and several commands in the Process ▸ Math submenu. If you need a sample image, you can use File ▸ Open Samples ▸ Blobs (25K). (Be sure to pay attention to the bit-depth!)


The process to calculate the gradient magnitude is:

  1. Convert the image to 32-bit and duplicate it

  2. Convolve one copy of the image with the horizontal gradient filter, and one with the vertical

  3. Compute the square of both images (Process ▸ Math ▸ Square)

  4. Use the image calculator to add the images together

  5. Compute the square root of the resulting image (Process ▸ Math ▸ Square Root)

Note that this process goes wrong if the image is stored in some unsigned integer format, since it needs negative values.


Suppose the mean pixel value of an image is 100. What will the mean value be after applying a horizontal gradient filter?


After applying a gradient filter, the image mean will be 0: every pixel is added once and subtracted once when calculating the result. If you are sceptical about this: test it, making sure the image is 32-bit first.


There is a LUT called edges in ImageJ. Applied to File ▸ Open Samples ▸ Blobs (25K), it does a rather good job of highlighting edges – without actually changing the pixels at all. How does it work?


The edges LUT shows most low and high pixel values as black – and uses lighter shades of gray only for a small range of values in between (see Image ▸ Color ▸ Edit LUT…​). In any image with a good separation of background and foreground pixels, but which still has a somewhat smooth transition between them, this means everything but the edges can appear black.

Convolution & correlation

Although linear filtering and convolution are terms that are often used synonymously, the former is a quite general term while the latter can be used in a somewhat more restricted sense. Specifically, for convolution the filter should be rotated by 180 before applying the algorithm of Figure 4. If the algorithm is applied without the rotation, the result is really a correlation. However, this distinction is not always kept in practice; convolution is the more common term, and often used in image processing literature whenever no rotation is applied. Fortunately, much of the time we use symmetric filters, in which case it makes absolutely no difference which method is used. But for gradient filters, for example, it is good to be aware that the sign out the output (i.e. positive or negative) would be affected.

Why rotate a filter for convolution?

It may not be entirely clear why rotating a filter for convolution would be worthwhile. One partial explanation is that if you convolve a filter with an image containing only a single non-zero pixel that has a value of one, the result is an exact replica of the filter. But if you correlate the filter with the same image, the result is a rotated version of the filter. This can be inferred from Figures 4A and 4B: you can see that when the bottom right value of the filter overlaps with the first non-zero pixel, it results in the filter coefficient’s value being inserted in the top left of the image. Thus the application of the algorithm in Figure 4 inherently involves a rotation, and by rotating the filter first this is simply 'corrected'.

This leads to an equivalent way to think of convolution: each pixel value in an image scales the filter, and then these scaled filters replace the original pixels in the image – with overlapping values added together (Figure 6). This idea reappears in Blur & the PSF, because convolution happens to also describe the blur inherent in light microscopy.

conv alt 1.png
A: A copy of the filter is centered on every non-zero pixel in the image, and its coefficients are multiplied by the value of that pixel.
conv alt 2.png
B: The coefficients of the scaled filters are assigned to the pixels of the output image, and overlapping values added together.

Figure 6: An alternative view of convolution as the summation of many scaled filters. Here, only two pixels in the original image have non-zero values so only two copies of the filter are needed, but often all pixels are non-zero – resulting in the addition of as many scaled filters as there are pixels. The final image computed this way is the same as that obtained by the method in Figure 4 – assuming either symmetrical filters, or that one of them has been rotated.


Does ImageJ’s Convolve…​ command really implement convolution – or is it actually correlation?


At the time of writing, the Convolve…​ command actually applies correlation!

Filtering at image boundaries

filter corner.png If a filter consists of more than one coefficient, the neighborhood will extend beyond the image boundaries when filtering some pixels nearby. These boundary pixels could simply be ignored and left with their original values, but for large neighborhoods this would result in much of the image being unfiltered. Alternative options include treating every pixel beyond the boundary as zero, replicating the closest valid pixel, treating the image as if it is part of a periodic tiling, or mirroring the internal values (Figure 7).

boundary original.png
A: Original image
boundary zeros.png
B: Zeros
boundary replicate.png
C: Replication
boundary circular.png
D: Zeros
boundary symmetric.png
E: Replication

Figure 7: Methods for determining suitable values for pixels beyond image boundaries when filtering.


Using any image, work out which of the methods for dealing with boundaries shown in Figure 7 is used by ImageJ’s Convolve…​ command.


Replication of boundary pixels is the default method used by Convolve…​ in ImageJ (although other filtering plugins by different authors might use different methods).

My approach to test this involved using Convolve…​ with a filter that consisting of a 1 followed by a lot of zeros (e.g. 1 0 0 0 0 0 0 0 0 0 0 0 0). This basically shifts the image to the right, bringing whatever is outside the image boundary into view.

Nonlinear filters

Linear filters involve taking neighborhoods of pixels, scaling them by specified constants, and adding the results to get new pixel values. Nonlinear filters also make use of neighborhoods of pixels, but with different calculations to obtain the output. Here we will consider one especially important family of nonlinear filters.

Rank filters

Rank filters effectively sort the values of all the neighboring pixels in ascending order, and then choose the output based upon this ordered list. The most common example is the median filter, in which the pixel value at the center of the list is used for the filtered output.

rank results.png

Figure 8: Results of different 3 × 3 rank filters when processing a single neighborhood in an image. The output of a 3 × 3 mean filter in this case would also be 15.

The result of applying a median filter is often similar to that of applying a mean filter, but has the major advantage of removing extreme isolated values completely, without allowing them to have an impact upon surrounding pixels. This is in contrast to a mean filter, which cannot ignore extreme pixels but rather will smooth them out into occupying larger regions (Figure 9). However, a disadvantage of a median filter is that it can seem to introduce patterns or textures that were not present in the original image, at least whenever the size of the filter increases (see Figure 13D). Another disadvantage is that large median filters tend to be slow.

filters speckled mean.png
A: Mean filter
filters speckled median.png
B: Median filter

Figure 9: Applying mean and median filters (radius = 1 pixel) to an image containing isolated extreme values (known as salt and pepper noise). A mean filter reduces the intensity of the extreme values but spreads out their influence, while a small median filter is capable of removing them completely with a minimal effect upon the rest of the image.

Other rank filters include the minimum and maximum filters, which replace each pixel value with the minimum or maximum value in the surrounding neighborhood respectively (Figure 10). They will become more important when we discuss Binary images.

filters median orig.png
A: Median filter
filters maximum orig.png
B: Maximum filter
filters minimum orig.png
C: Minimum filter

Figure 10: The result of applying three rank filters (radius = 1 pixel) to the noise-free image in Figure 13A.


What would happen if you subtract a minimum filtered image (e.g. Figure 10C) from a maximum filtered image (Figure 10B)?


Subtracting a minimum from a maximum filtered image would be another way to accent the edges:

filters maximum minus minimum.png
Removing outliers

Figure 9 shows that median filtering is much better than mean filtering for removing outliers. We might encounter this if something in the microscope is not quite functioning as expected or if dark noise is a problem, but otherwise we expect the noise in fluorescence microscopy images to produce few really extreme values (see Noise).

Nevertheless, Process ▸ Noise ▸ Remove Outliers…​ provides an alternative if isolated bright values are present. This is a nonlinear filter that inserts median values only whenever a pixel is found that is further away from the local median than some adjustable threshold. It is therefore like a more selective median filter that will only modify the image at pixels where it is considered really necessary. The main difficulty is then choosing a sensible threshold.

Gaussian filters

Gaussian filters from Gaussian functions

We end this chapter with one fantastically important linear filter, and some variants based upon it. A Gaussian filter is a linear filter that also smooths an image and reduces noise. However, unlike a mean filter – for which even the furthest away pixels in the neighborhood influence the result by the same amount as the closest pixels – the smoothing of a Gaussian filter is weighted so that the influence of a pixel decreases with its distance from the filter center. This tends to give a better result in many cases (Figure 11).

filt dots.png
A: Original dots
filt dots mean.png
B: Mean filtered
filt dots gaussian.png
C: Gaussian filtered

Figure 11: Comparing a mean and Gaussian filter. The mean filter can introduce patterns and maxima where previously there were none. For example, the brightest region in (B) is one such maximum – but the values of all pixels in the same region in (A) were zero! By contrast, the Gaussian filter produced a smoother, more visually pleasing result, less prone to this effect (C).

The coefficients of a Gaussian filter are determined from a Gaussian function (Figure 12), and its size is controlled by a value – so when working with ImageJ’s Gaussian Blur…​ command, you will need to specify this rather than the filter radius. is equivalent to the standard deviation of a normal (i.e. Gaussian) distribution.

maths gaussian 2d.png

Figure 12: Surface plot of a 2D Gaussian function, calculated using the equation

The scaling factor is used to make the entire volume under the surface equal to 1, which in terms of filtering means that the coefficients add to 1 and the image will not be unexpectedly scaled. The size of the function is controlled by .

A comparison of several filters is shown in Figure 13.

filters orig.png
A: Original, noise-free image
filters noisy.png
B: Noisy image
filters mean 2.png
C: Mean filtered, radius = 2
filters median 2.png
D: Median filtered, radius = 2
filters gaussian 1.png
E: Gaussian filtered,
filters gaussian 2 5.png
F: Gaussian filtered,

Figure 13: The effects of various linear and nonlinear filters upon a noisy image of a fixed cell.

Filters of varying sizes

Gaussian filters have useful properties that make them generally preferable to mean filters, some of which will be mentioned in Blur & the PSF (others require a trip into Fourier space, beyond the scope of this book). Therefore if in doubt regarding which filter to use for smoothing, Gaussian is likely to be the safer choice. Nevertheless, your decisions are not at an end since the size of the filter still needs to be chosen.

A small filter will mostly suppress noise, because noise masquerades as tiny fluorescence fluctuations at individual pixels. As the filter size increases, Gaussian filtering starts to suppress larger structures occupying multiple pixels – reducing their intensities and increasing their sizes, until eventually they would be smoothed into surrounding regions (Figure 14). By varying the filter size, we can then decide the scale at which the processing and analysis should happen.

gaussian effects sigma 0.png
A: Original image
gaussian effects sigma 2.png
B: Gaussian
gaussian effects sigma 5.png
C: Gaussian
gaussian effects plot.png
D: Profile plots of the intensity in the red channel of the image

Figure 14: The effect of Gaussian filtering on the size and intensity of structures. The image is taken from File ▸ Open Samples ▸ HeLa Cells, with some additional simulated noise added to show that this is also reduced by Gaussian filtering.

Figure 15 shows an example of when this is useful. Here, gradient magnitude images are computed similar to that in Figure 5, but because the original image is now noisy the initial result is not very useful – with even strong edges being buried amid noise (B). Applying a small Gaussian filter prior to computing the gradient magnitude gives much better results (C), but if we only wanted the very strongest edges then a larger filter would be better (D).

edge sigma orig.png
A: Original image
edge sigma 0.png
B: No smoothing
edge sigma 2.png
C: Gaussian
edge sigma 5.png
D: Gaussian

Figure 15: Applying Gaussian filters before computing the gradient magnitude changes the scale at which edges are enhanced.

Difference of Gaussians filtering

So Gaussian filters can be chosen to suppress small structures. But what if we also wish to suppress large structures – so that we can concentrate on detecting or measuring structures with sizes inside a particular range?

We already have the pieces necessary to construct one solution. Suppose we apply one Gaussian filter to reduce small structures. Then we apply a second Gaussian filter, bigger than the first, to a duplicate of the image. This will remove even more structures, while still preserving the largest features in the image. But if we finally subtract this second filtered image from the first, we are left with an image that contains the information that 'falls between' the two smoothing scales we used. This process is called difference of Gaussians (DoG) filtering, and it is especially useful for detecting small spots or as an alternative to the gradient magnitude for enhancing edges (Figure 16).

dog red.png
A: Original image
dog red 1 2.png
B: DoG, 1, 2
dog red 2 4.png
C: DoG, 2, 4
dog red 4 8.png
D: DoG, 4, 8

Figure 16: Difference of Gaussian filtering of the same image at various scales.

DoG filters

In fact, to get the result of DoG filtering it is not necessary to filter the image twice and subtract the results. We could equally well subtract the coefficients of the larger filter from the smaller first (after making sure both filters are the same size by adding zeros to the edges as required), then apply the resulting filter to the image only once (Figure 17).

dog plot small.png
A: Small Gaussian filter
dog plot large.png
B: Larger Gaussian filter
dog plot diff.png
C: DoG filter

Figure 17: Surface plots of two Gaussian filters with small and large , and the result of subtracting the latter from the former. The sum of the coefficients for and is one in each case, while the coefficients of add to zero.

Laplacian of Gaussian filtering

One minor complication with DoG filtering is the need to select two different values of . A similar operation, which requires only a single and a single filter, is Laplacian of Gaussian (LoG) filtering. The appearance of a LoG filter is like an upside-down DoG filter (Figure 18), but if the resulting filtered image is inverted then the results are comparable[4]. You can test out LoG filtering in Fiji using Plugins ▸ Feature Extraction ▸ FeatureJ ▸ FeatureJ Laplacian[5].

log plot.png
A: Laplacian of Gaussian filter
log plot inverted.png
B: Inverted version of

Figure 18: Surface plot of a LoG filter. This closely resembles Figure 17, but inverted so that the negative values are found in the filter center.

dog on log orig.png
A: Original image
dog on log gaussians.png
B: DoG filtered
dog on log laplacian.png
C: LoG filtered

Figure 19: Application of DoG and LoG filtering to an image. Both methods enhance the appearance of spot-like structures, and (to a lesser extent) edges, and result in an image containing both positive and negative values with an overall mean of zero. In the case of LoG filtering, inversion is involved: darker points become bright after filtering.


Why does the FeatureJ Laplacian command have a Detect zero-crossings option? If you are unsure, you can try it out on DoG_on_LoG.png. You should also investigate the effects of changing the Smoothing scale option.


After computing the LoG filtered image, Detect zero-crossings identifies locations where the sign of adjacent pixels changes from negative-to-positive (or vice versa). Thanks to the properties of the Laplacian operator, these correspond to edges. Unfortunately, they often give rather a lot more edges than you might like – but by increasing the Smoothing scale parameter, the sigma value is increased to cause more smoothing and thereby reduce the edges caused by small structures or noise (see Figure 15).

Unsharp masking

Finally, a related technique widely-used to enhance the visibility of details in images – although not advisable for quantitative analysis – is unsharp masking (Process ▸ Filters ▸ Unsharp mask…​).

This uses a Gaussian filter first to blur the edges of an image, and then subtracts it from the original. But rather than stop there, the subtracted image is multiplied by some weighting factor and added back to the original. This gives an image that looks much the same as the original, but with edges sharpened by an amount dependent upon the chosen weight.

unsharp masking orig.png
A: Original image
unsharp masking 1 subtract.png
B: Gaussian subtracted
unsharp masking 1 09.png
C: Unsharp masked

Figure 20: The application of unsharp masking to a blurred image. First a Gaussian-smoothed version of the image () is subtracted from the original, scaled () and added back to the original.

Unsharp masking can improve the visual appearance of an image, but it is important to remember that it modifies the image content in a way that might well be considered suspicious in scientific circles. Therefore, if you apply unsharp masking to any image you intend to share with the world you should have a good justification and certainly admit what you have done. If you want a more theoretically justified method to improve image sharpness, it may be worth looking into '(maximum likelihood) deconvolution' algorithms.

1. Also called an arithmetic mean, averaging or box-car filter.
2. Note that the shortcut is Shift+S – a fact I rediscover regularly when intending to save my images. Be careful!
3. The equation then looks like Pythagoras' theorem:
4. A LoG filter is also often referred to as a mexican-hat filter, although clearly the filter (or the hat-wearer) should be inverted for the name to make more sense.
5. This now requires the use of a separate update site - see for installation information.

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